Agent-based simulation of dynamic online auctions
Proceedings of the 32nd conference on Winter simulation
Simulating Online Yankee Auctions to Optimize Sellers Revenue
HICSS '01 Proceedings of the 34th Annual Hawaii International Conference on System Sciences ( HICSS-34)-Volume 7 - Volume 7
Running up the bid: detecting, predicting, and preventing reserve price shilling in online auctions
ICEC '03 Proceedings of the 5th international conference on Electronic commerce
Netprobe: a fast and scalable system for fraud detection in online auction networks
Proceedings of the 16th international conference on World Wide Web
An Empirical Analysis of Fraud Detection in Online Auctions: Credit Card Phantom Transaction
HICSS '07 Proceedings of the 40th Annual Hawaii International Conference on System Sciences
ITNG '07 Proceedings of the International Conference on Information Technology
Detecting Collusive Shill Bidding
ITNG '07 Proceedings of the International Conference on Information Technology
The Role of Reputation Systems in Reducing On-Line Auction Fraud
International Journal of Electronic Commerce
The effects of shilling on final bid prices in online auctions
Electronic Commerce Research and Applications
eBay: an E-commerce marketplace as a complex network
Proceedings of the fourth ACM international conference on Web search and data mining
A novel two-stage phased modeling framework for early fraud detection in online auctions
Expert Systems with Applications: An International Journal
Reputation inflation detection in a Chinese C2C market
Electronic Commerce Research and Applications
Detecting fraudulent personalities in networks of online auctioneers
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
A Supervised Learning Process to Elicit Fraud Cases in Online Auction Sites
SYNASC '11 Proceedings of the 2011 13th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing
Survey: Combating online in-auction fraud: Clues, techniques and challenges
Computer Science Review
Generating realistic online auction data
AI'12 Proceedings of the 25th Australasian joint conference on Advances in Artificial Intelligence
Evaluating Fraud Detection Algorithms Using an Auction Data Generator
ICDMW '12 Proceedings of the 2012 IEEE 12th International Conference on Data Mining Workshops
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
Online auction sites are a target for fraud due to their anonymity, number of potential targets and low likelihood of identification. Researchers have developed methods for identifying fraud. However, these methods must be individually tailored for each type of fraud, since each differs in the characteristics important for their identification. Using supervised learning methods, it is possible to produce classifiers for specific types of fraud by providing a dataset where instances with behaviours of interest are assigned to a separate class. However this requires multiple labelled datasets: one for each fraud type of interest. It is difficult to use real-world datasets for this purpose since they are difficult to label, often limited in size, and contain zero or multiple suspicious behaviours that may or may not be under investigation. The aims of this work are to: (1) demonstrate the approach of using supervised learning together with a validated synthetic data generator to create fraud detection models that are experimentally more accurate than existing methods and that is effective over real data, and (2) to evaluate a set of features for use in general fraud detection is shown to further improve the performance of the created detection models. The approach is as follows: the data generator is an agent-based simulation modelled on users in commercial online auction data. The simulation is extended using fraud agents which model a known type of online auction fraud called competitive shilling. These agents are added to the simulation to produce the synthetic datasets. Features extracted from this data are used as training data for supervised learning. Using this approach, we optimise an existing fraud detection algorithm, and produce classifiers capable of detecting shilling fraud. Experimental results with synthetic data show the new models have significant improvements in detection accuracy. Results with commercial data show the models identify users with suspicious behaviour.